Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations2256
Missing cells12610
Missing cells (%)26.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory808.1 KiB
Average record size in memory366.8 B

Variable types

DateTime2
Numeric14
Text1
Categorical2
Unsupported2

Alerts

city_name has constant value "local"Constant
Autoconsumo (kWh) is highly overall correlated with Horário Económico (kWh)High correlation
Hora is highly overall correlated with Normal (kWh)High correlation
Horário Económico (kWh) is highly overall correlated with Autoconsumo (kWh) and 1 other fieldsHigh correlation
Normal (kWh) is highly overall correlated with Hora and 1 other fieldsHigh correlation
clouds_all is highly overall correlated with humidity and 1 other fieldsHigh correlation
feels_like is highly overall correlated with temp and 2 other fieldsHigh correlation
humidity is highly overall correlated with clouds_allHigh correlation
rain_1h is highly overall correlated with weather_descriptionHigh correlation
temp is highly overall correlated with feels_like and 2 other fieldsHigh correlation
temp_max is highly overall correlated with feels_like and 2 other fieldsHigh correlation
temp_min is highly overall correlated with feels_like and 2 other fieldsHigh correlation
weather_description is highly overall correlated with clouds_all and 1 other fieldsHigh correlation
dt has 504 (22.3%) missing valuesMissing
dt_iso has 504 (22.3%) missing valuesMissing
city_name has 504 (22.3%) missing valuesMissing
temp has 504 (22.3%) missing valuesMissing
feels_like has 504 (22.3%) missing valuesMissing
temp_min has 504 (22.3%) missing valuesMissing
temp_max has 504 (22.3%) missing valuesMissing
pressure has 504 (22.3%) missing valuesMissing
sea_level has 2256 (100.0%) missing valuesMissing
grnd_level has 2256 (100.0%) missing valuesMissing
humidity has 504 (22.3%) missing valuesMissing
wind_speed has 504 (22.3%) missing valuesMissing
rain_1h has 2050 (90.9%) missing valuesMissing
clouds_all has 504 (22.3%) missing valuesMissing
weather_description has 504 (22.3%) missing valuesMissing
dt is uniformly distributedUniform
DataHora has unique valuesUnique
sea_level is an unsupported type, check if it needs cleaning or further analysisUnsupported
grnd_level is an unsupported type, check if it needs cleaning or further analysisUnsupported
Hora has 94 (4.2%) zerosZeros
Normal (kWh) has 1225 (54.3%) zerosZeros
Horário Económico (kWh) has 1317 (58.4%) zerosZeros
Autoconsumo (kWh) has 1263 (56.0%) zerosZeros
clouds_all has 504 (22.3%) zerosZeros

Reproduction

Analysis started2025-11-02 21:13:31.528028
Analysis finished2025-11-02 21:14:08.177513
Duration36.65 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

DataHora
Date

Unique 

Distinct2256
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Minimum2023-01-01 00:00:00
Maximum2023-04-04 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-02T21:14:08.253324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:08.523408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Data
Date

Distinct94
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size17.8 KiB
Minimum2023-01-01 00:00:00
Maximum2023-04-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-02T21:14:08.841745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:09.149055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Hora
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros94
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:09.397145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9237212
Coefficient of variation (CV)0.60206272
Kurtosis-1.2041827
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum25944
Variance47.937916
MonotonicityNot monotonic
2025-11-02T21:14:09.577433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
094
 
4.2%
194
 
4.2%
294
 
4.2%
394
 
4.2%
494
 
4.2%
594
 
4.2%
694
 
4.2%
794
 
4.2%
894
 
4.2%
994
 
4.2%
Other values (14)1316
58.3%
ValueCountFrequency (%)
094
4.2%
194
4.2%
294
4.2%
394
4.2%
494
4.2%
594
4.2%
694
4.2%
794
4.2%
894
4.2%
994
4.2%
ValueCountFrequency (%)
2394
4.2%
2294
4.2%
2194
4.2%
2094
4.2%
1994
4.2%
1894
4.2%
1794
4.2%
1694
4.2%
1594
4.2%
1494
4.2%

Normal (kWh)
Real number (ℝ)

High correlation  Zeros 

Distinct709
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26805984
Minimum0
Maximum3.381
Zeros1225
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:09.816044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.39875
95-th percentile1.27225
Maximum3.381
Range3.381
Interquartile range (IQR)0.39875

Descriptive statistics

Standard deviation0.46432336
Coefficient of variation (CV)1.7321631
Kurtosis7.1225225
Mean0.26805984
Median Absolute Deviation (MAD)0
Skewness2.4579907
Sum604.743
Variance0.21559618
MonotonicityNot monotonic
2025-11-02T21:14:10.095825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01225
54.3%
0.2227
 
0.3%
0.0076
 
0.3%
0.5075
 
0.2%
0.1395
 
0.2%
0.3385
 
0.2%
0.4315
 
0.2%
0.6375
 
0.2%
0.5645
 
0.2%
0.2874
 
0.2%
Other values (699)984
43.6%
ValueCountFrequency (%)
01225
54.3%
0.0011
 
< 0.1%
0.0021
 
< 0.1%
0.0031
 
< 0.1%
0.0041
 
< 0.1%
0.0051
 
< 0.1%
0.0063
 
0.1%
0.0076
 
0.3%
0.0094
 
0.2%
0.014
 
0.2%
ValueCountFrequency (%)
3.3811
< 0.1%
2.9481
< 0.1%
2.8451
< 0.1%
2.7821
< 0.1%
2.771
< 0.1%
2.7641
< 0.1%
2.6511
< 0.1%
2.5961
< 0.1%
2.5331
< 0.1%
2.5061
< 0.1%

Horário Económico (kWh)
Real number (ℝ)

High correlation  Zeros 

Distinct490
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22680895
Minimum0
Maximum2.771
Zeros1317
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:10.331034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.336
95-th percentile0.97675
Maximum2.771
Range2.771
Interquartile range (IQR)0.336

Descriptive statistics

Standard deviation0.37575217
Coefficient of variation (CV)1.6566902
Kurtosis9.2471322
Mean0.22680895
Median Absolute Deviation (MAD)0
Skewness2.6471707
Sum511.681
Variance0.14118969
MonotonicityNot monotonic
2025-11-02T21:14:10.612517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01317
58.4%
0.24410
 
0.4%
0.2469
 
0.4%
0.3059
 
0.4%
0.2548
 
0.4%
0.3068
 
0.4%
0.2568
 
0.4%
0.2517
 
0.3%
0.37
 
0.3%
0.2977
 
0.3%
Other values (480)866
38.4%
ValueCountFrequency (%)
01317
58.4%
0.0531
 
< 0.1%
0.1451
 
< 0.1%
0.2091
 
< 0.1%
0.2231
 
< 0.1%
0.2241
 
< 0.1%
0.2291
 
< 0.1%
0.2332
 
0.1%
0.2343
 
0.1%
0.2353
 
0.1%
ValueCountFrequency (%)
2.7711
< 0.1%
2.71
< 0.1%
2.6721
< 0.1%
2.5271
< 0.1%
2.4991
< 0.1%
2.4781
< 0.1%
2.4721
< 0.1%
2.4681
< 0.1%
2.2151
< 0.1%
2.2071
< 0.1%

Autoconsumo (kWh)
Real number (ℝ)

High correlation  Zeros 

Distinct485
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11943883
Minimum0
Maximum1.161
Zeros1263
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:10.847312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.23725
95-th percentile0.50525
Maximum1.161
Range1.161
Interquartile range (IQR)0.23725

Descriptive statistics

Standard deviation0.19060084
Coefficient of variation (CV)1.595803
Kurtosis3.7519876
Mean0.11943883
Median Absolute Deviation (MAD)0
Skewness1.880032
Sum269.454
Variance0.036328681
MonotonicityNot monotonic
2025-11-02T21:14:10.983729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01263
56.0%
0.00113
 
0.6%
0.00311
 
0.5%
0.0048
 
0.4%
0.0028
 
0.4%
0.2517
 
0.3%
0.2556
 
0.3%
0.2416
 
0.3%
0.0076
 
0.3%
0.3056
 
0.3%
Other values (475)922
40.9%
ValueCountFrequency (%)
01263
56.0%
0.00113
 
0.6%
0.0028
 
0.4%
0.00311
 
0.5%
0.0048
 
0.4%
0.0056
 
0.3%
0.0065
 
0.2%
0.0076
 
0.3%
0.0084
 
0.2%
0.0093
 
0.1%
ValueCountFrequency (%)
1.1611
< 0.1%
1.071
< 0.1%
1.061
< 0.1%
0.9952
0.1%
0.9811
< 0.1%
0.9761
< 0.1%
0.9621
< 0.1%
0.9541
< 0.1%
0.9521
< 0.1%
0.9151
< 0.1%

dt
Real number (ℝ)

Missing  Uniform 

Distinct1752
Distinct (%)100.0%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean1.675683 × 109
Minimum1.6725312 × 109
Maximum1.6788348 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:11.077662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.6725312 × 109
5-th percentile1.6728464 × 109
Q11.6741071 × 109
median1.675683 × 109
Q31.6772589 × 109
95-th percentile1.6785196 × 109
Maximum1.6788348 × 109
Range6303600
Interquartile range (IQR)3151800

Descriptive statistics

Standard deviation1821251.4
Coefficient of variation (CV)0.0010868711
Kurtosis-1.2
Mean1.675683 × 109
Median Absolute Deviation (MAD)1576800
Skewness-1.9848613 × 10-16
Sum2.9357966 × 1012
Variance3.3169565 × 1012
MonotonicityStrictly increasing
2025-11-02T21:14:11.253242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16725708001
 
< 0.1%
16725744001
 
< 0.1%
16725780001
 
< 0.1%
16725816001
 
< 0.1%
16725852001
 
< 0.1%
16725888001
 
< 0.1%
16725924001
 
< 0.1%
16725960001
 
< 0.1%
16725996001
 
< 0.1%
16726032001
 
< 0.1%
Other values (1742)1742
77.2%
(Missing)504
 
22.3%
ValueCountFrequency (%)
16725312001
< 0.1%
16725348001
< 0.1%
16725384001
< 0.1%
16725420001
< 0.1%
16725456001
< 0.1%
16725492001
< 0.1%
16725528001
< 0.1%
16725564001
< 0.1%
16725600001
< 0.1%
16725636001
< 0.1%
ValueCountFrequency (%)
16788348001
< 0.1%
16788312001
< 0.1%
16788276001
< 0.1%
16788240001
< 0.1%
16788204001
< 0.1%
16788168001
< 0.1%
16788132001
< 0.1%
16788096001
< 0.1%
16788060001
< 0.1%
16788024001
< 0.1%

dt_iso
Text

Missing 

Distinct1752
Distinct (%)100.0%
Missing504
Missing (%)22.3%
Memory size149.3 KiB
2025-11-02T21:14:11.948319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters50808
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1752 ?
Unique (%)100.0%

Sample

1st row2023-01-01 00:00:00 +0000 UTC
2nd row2023-01-01 01:00:00 +0000 UTC
3rd row2023-01-01 02:00:00 +0000 UTC
4th row2023-01-01 03:00:00 +0000 UTC
5th row2023-01-01 04:00:00 +0000 UTC
ValueCountFrequency (%)
00001752
25.0%
utc1752
25.0%
11:00:0073
 
1.0%
10:00:0073
 
1.0%
12:00:0073
 
1.0%
13:00:0073
 
1.0%
14:00:0073
 
1.0%
15:00:0073
 
1.0%
16:00:0073
 
1.0%
17:00:0073
 
1.0%
Other values (89)2920
41.7%
2025-11-02T21:14:12.416973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
019261
37.9%
25335
 
10.5%
5256
 
10.3%
:3504
 
6.9%
-3504
 
6.9%
32547
 
5.0%
12509
 
4.9%
+1752
 
3.4%
U1752
 
3.4%
T1752
 
3.4%
Other values (7)3636
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)50808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019261
37.9%
25335
 
10.5%
5256
 
10.3%
:3504
 
6.9%
-3504
 
6.9%
32547
 
5.0%
12509
 
4.9%
+1752
 
3.4%
U1752
 
3.4%
T1752
 
3.4%
Other values (7)3636
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)50808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019261
37.9%
25335
 
10.5%
5256
 
10.3%
:3504
 
6.9%
-3504
 
6.9%
32547
 
5.0%
12509
 
4.9%
+1752
 
3.4%
U1752
 
3.4%
T1752
 
3.4%
Other values (7)3636
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)50808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019261
37.9%
25335
 
10.5%
5256
 
10.3%
:3504
 
6.9%
-3504
 
6.9%
32547
 
5.0%
12509
 
4.9%
+1752
 
3.4%
U1752
 
3.4%
T1752
 
3.4%
Other values (7)3636
 
7.2%

city_name
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing504
Missing (%)22.3%
Memory size120.1 KiB
local
1752 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters8760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlocal
2nd rowlocal
3rd rowlocal
4th rowlocal
5th rowlocal

Common Values

ValueCountFrequency (%)
local1752
77.7%
(Missing)504
 
22.3%

Length

2025-11-02T21:14:12.500900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T21:14:12.542835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
local1752
100.0%

Most occurring characters

ValueCountFrequency (%)
l3504
40.0%
o1752
20.0%
c1752
20.0%
a1752
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l3504
40.0%
o1752
20.0%
c1752
20.0%
a1752
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l3504
40.0%
o1752
20.0%
c1752
20.0%
a1752
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l3504
40.0%
o1752
20.0%
c1752
20.0%
a1752
20.0%

temp
Real number (ℝ)

High correlation  Missing 

Distinct889
Distinct (%)50.7%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean10.599606
Minimum0.93
Maximum20.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:12.609436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.93
5-th percentile4.1955
Q17.8775
median10.95
Q313.2925
95-th percentile16.39
Maximum20.61
Range19.68
Interquartile range (IQR)5.415

Descriptive statistics

Standard deviation3.7151563
Coefficient of variation (CV)0.35049947
Kurtosis-0.43883227
Mean10.599606
Median Absolute Deviation (MAD)2.6
Skewness-0.11427813
Sum18570.51
Variance13.802386
MonotonicityNot monotonic
2025-11-02T21:14:13.014232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9818
 
0.8%
13.4916
 
0.7%
14.5914
 
0.6%
12.9112
 
0.5%
12.411
 
0.5%
11.2910
 
0.4%
12.389
 
0.4%
13.559
 
0.4%
11.98
 
0.4%
13.018
 
0.4%
Other values (879)1637
72.6%
(Missing)504
 
22.3%
ValueCountFrequency (%)
0.931
< 0.1%
1.041
< 0.1%
1.121
< 0.1%
1.221
< 0.1%
1.781
< 0.1%
1.811
< 0.1%
1.981
< 0.1%
2.261
< 0.1%
2.271
< 0.1%
2.291
< 0.1%
ValueCountFrequency (%)
20.611
< 0.1%
20.471
< 0.1%
20.451
< 0.1%
20.31
< 0.1%
20.251
< 0.1%
20.211
< 0.1%
19.962
0.1%
19.721
< 0.1%
19.72
0.1%
19.571
< 0.1%

feels_like
Real number (ℝ)

High correlation  Missing 

Distinct1007
Distinct (%)57.5%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean9.5432648
Minimum-1.79
Maximum19.79
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)0.5%
Memory size17.8 KiB
2025-11-02T21:14:13.304466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.79
5-th percentile2.3255
Q16.2175
median10.135
Q312.7425
95-th percentile15.609
Maximum19.79
Range21.58
Interquartile range (IQR)6.525

Descriptive statistics

Standard deviation4.1912244
Coefficient of variation (CV)0.4391814
Kurtosis-0.60687299
Mean9.5432648
Median Absolute Deviation (MAD)3.09
Skewness-0.25248791
Sum16719.8
Variance17.566362
MonotonicityNot monotonic
2025-11-02T21:14:13.595691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.368
 
0.4%
11.778
 
0.4%
10.938
 
0.4%
12.798
 
0.4%
15.117
 
0.3%
9.587
 
0.3%
12.857
 
0.3%
10.277
 
0.3%
12.27
 
0.3%
12.187
 
0.3%
Other values (997)1678
74.4%
(Missing)504
 
22.3%
ValueCountFrequency (%)
-1.791
< 0.1%
-1.751
< 0.1%
-1.491
< 0.1%
-0.871
< 0.1%
-0.731
< 0.1%
-0.571
< 0.1%
-0.241
< 0.1%
-0.221
< 0.1%
-0.112
0.1%
-0.091
< 0.1%
ValueCountFrequency (%)
19.791
< 0.1%
19.71
< 0.1%
19.511
< 0.1%
19.481
< 0.1%
19.331
< 0.1%
19.251
< 0.1%
19.211
< 0.1%
19.21
< 0.1%
19.031
< 0.1%
18.921
< 0.1%

temp_min
Real number (ℝ)

High correlation  Missing 

Distinct169
Distinct (%)9.6%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean9.1779167
Minimum-0.85
Maximum20.01
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)0.6%
Memory size17.8 KiB
2025-11-02T21:14:13.842682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.85
5-th percentile1.963
Q16.2475
median9.71
Q312.32
95-th percentile14.72
Maximum20.01
Range20.86
Interquartile range (IQR)6.0725

Descriptive statistics

Standard deviation4.0211764
Coefficient of variation (CV)0.43813608
Kurtosis-0.60244543
Mean9.1779167
Median Absolute Deviation (MAD)3.01
Skewness-0.28607629
Sum16079.71
Variance16.16986
MonotonicityNot monotonic
2025-11-02T21:14:14.107062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.7941
 
1.8%
12.2338
 
1.7%
10.6636
 
1.6%
13.4334
 
1.5%
9.5433
 
1.5%
9.2332
 
1.4%
7.5732
 
1.4%
12.7231
 
1.4%
4.7128
 
1.2%
11.2128
 
1.2%
Other values (159)1419
62.9%
(Missing)504
 
22.3%
ValueCountFrequency (%)
-0.853
 
0.1%
-0.298
0.4%
-0.281
 
< 0.1%
-0.211
 
< 0.1%
0.266
0.3%
0.342
 
0.1%
0.7210
0.4%
0.829
0.4%
0.910
0.4%
1.388
0.4%
ValueCountFrequency (%)
20.011
 
< 0.1%
19.451
 
< 0.1%
18.91
 
< 0.1%
18.722
 
0.1%
18.61
 
< 0.1%
18.342
 
0.1%
17.792
 
0.1%
17.726
0.3%
17.371
 
< 0.1%
17.324
0.2%

temp_max
Real number (ℝ)

High correlation  Missing 

Distinct174
Distinct (%)9.9%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean11.643813
Minimum3.34
Maximum22.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:14.359202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.34
5-th percentile5.66
Q18.9
median11.82
Q314.04
95-th percentile17.01
Maximum22.01
Range18.67
Interquartile range (IQR)5.14

Descriptive statistics

Standard deviation3.5968823
Coefficient of variation (CV)0.30890932
Kurtosis-0.3703718
Mean11.643813
Median Absolute Deviation (MAD)2.72
Skewness-0.01743194
Sum20399.96
Variance12.937563
MonotonicityNot monotonic
2025-11-02T21:14:14.581821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.8295
 
4.2%
11.8274
 
3.3%
12.8272
 
3.2%
10.8249
 
2.2%
14.8244
 
2.0%
15.8234
 
1.5%
6.8229
 
1.3%
9.8226
 
1.2%
13.7226
 
1.2%
8.8226
 
1.2%
Other values (164)1277
56.6%
(Missing)504
 
22.3%
ValueCountFrequency (%)
3.341
 
< 0.1%
3.432
 
0.1%
3.721
 
< 0.1%
3.92
 
0.1%
3.996
0.3%
4.042
 
0.1%
4.231
 
< 0.1%
4.459
0.4%
4.5410
0.4%
4.594
 
0.2%
ValueCountFrequency (%)
22.012
 
0.1%
21.821
 
< 0.1%
21.452
 
0.1%
21.382
 
0.1%
21.121
 
< 0.1%
20.92
 
0.1%
20.823
0.1%
20.72
 
0.1%
20.345
0.2%
20.263
0.1%

pressure
Real number (ℝ)

Missing 

Distinct31
Distinct (%)1.8%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean1023.4937
Minimum1006
Maximum1036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:14.811598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1006
5-th percentile1010
Q11020
median1024
Q31029
95-th percentile1033
Maximum1036
Range30
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.6508731
Coefficient of variation (CV)0.006498206
Kurtosis-0.38232493
Mean1023.4937
Median Absolute Deviation (MAD)4
Skewness-0.60862297
Sum1793161
Variance44.234112
MonotonicityNot monotonic
2025-11-02T21:14:15.013332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1028142
 
6.3%
1030117
 
5.2%
1024112
 
5.0%
1027111
 
4.9%
1029110
 
4.9%
1021100
 
4.4%
102395
 
4.2%
102688
 
3.9%
102581
 
3.6%
102075
 
3.3%
Other values (21)721
32.0%
(Missing)504
22.3%
ValueCountFrequency (%)
10063
 
0.1%
10076
 
0.3%
100824
1.1%
100927
1.2%
101038
1.7%
101131
1.4%
101242
1.9%
101332
1.4%
101429
1.3%
101528
1.2%
ValueCountFrequency (%)
10363
 
0.1%
10358
 
0.4%
103427
 
1.2%
103361
2.7%
103261
2.7%
103158
2.6%
1030117
5.2%
1029110
4.9%
1028142
6.3%
1027111
4.9%

sea_level
Unsupported

Missing  Rejected  Unsupported 

Missing2256
Missing (%)100.0%
Memory size17.8 KiB

grnd_level
Unsupported

Missing  Rejected  Unsupported 

Missing2256
Missing (%)100.0%
Memory size17.8 KiB

humidity
Real number (ℝ)

High correlation  Missing 

Distinct71
Distinct (%)4.1%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean75.63984
Minimum23
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:15.293620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile43
Q162
median81
Q391
95-th percentile95
Maximum97
Range74
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.415468
Coefficient of variation (CV)0.230242
Kurtosis-0.70029446
Mean75.63984
Median Absolute Deviation (MAD)13
Skewness-0.65459207
Sum132521
Variance303.29854
MonotonicityNot monotonic
2025-11-02T21:14:15.542434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94143
 
6.3%
95103
 
4.6%
9376
 
3.4%
9665
 
2.9%
8549
 
2.2%
8648
 
2.1%
9247
 
2.1%
8447
 
2.1%
9147
 
2.1%
9043
 
1.9%
Other values (61)1084
48.0%
(Missing)504
22.3%
ValueCountFrequency (%)
231
 
< 0.1%
252
 
0.1%
262
 
0.1%
301
 
< 0.1%
312
 
0.1%
323
 
0.1%
331
 
< 0.1%
342
 
0.1%
355
0.2%
369
0.4%
ValueCountFrequency (%)
972
 
0.1%
9665
2.9%
95103
4.6%
94143
6.3%
9376
3.4%
9247
 
2.1%
9147
 
2.1%
9043
 
1.9%
8942
 
1.9%
8832
 
1.4%

wind_speed
Real number (ℝ)

Missing 

Distinct512
Distinct (%)29.2%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean2.873613
Minimum0.12
Maximum10.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:15.772569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile1.02
Q11.8
median2.49
Q33.5
95-th percentile6.507
Maximum10.32
Range10.2
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.6204501
Coefficient of variation (CV)0.56390685
Kurtosis2.5172904
Mean2.873613
Median Absolute Deviation (MAD)0.78
Skewness1.4667235
Sum5034.57
Variance2.6258584
MonotonicityNot monotonic
2025-11-02T21:14:16.041876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.813
 
0.6%
2.8713
 
0.6%
1.8713
 
0.6%
1.9613
 
0.6%
2.4812
 
0.5%
1.7312
 
0.5%
1.7912
 
0.5%
1.8611
 
0.5%
2.211
 
0.5%
2.8311
 
0.5%
Other values (502)1631
72.3%
(Missing)504
 
22.3%
ValueCountFrequency (%)
0.121
< 0.1%
0.161
< 0.1%
0.221
< 0.1%
0.312
0.1%
0.341
< 0.1%
0.351
< 0.1%
0.371
< 0.1%
0.392
0.1%
0.421
< 0.1%
0.441
< 0.1%
ValueCountFrequency (%)
10.321
< 0.1%
9.871
< 0.1%
9.561
< 0.1%
9.531
< 0.1%
9.481
< 0.1%
9.381
< 0.1%
9.341
< 0.1%
9.251
< 0.1%
9.171
< 0.1%
9.121
< 0.1%

rain_1h
Real number (ℝ)

High correlation  Missing 

Distinct120
Distinct (%)58.3%
Missing2050
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean1.0250971
Minimum0.11
Maximum6.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:16.317597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.13
Q10.25
median0.565
Q31.245
95-th percentile3.63
Maximum6.38
Range6.27
Interquartile range (IQR)0.995

Descriptive statistics

Standard deviation1.2096175
Coefficient of variation (CV)1.1800028
Kurtosis5.2551679
Mean1.0250971
Median Absolute Deviation (MAD)0.395
Skewness2.2058189
Sum211.17
Variance1.4631744
MonotonicityNot monotonic
2025-11-02T21:14:16.590767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.157
 
0.3%
0.136
 
0.3%
0.115
 
0.2%
0.225
 
0.2%
0.194
 
0.2%
0.124
 
0.2%
0.314
 
0.2%
0.144
 
0.2%
0.574
 
0.2%
0.334
 
0.2%
Other values (110)159
 
7.0%
(Missing)2050
90.9%
ValueCountFrequency (%)
0.115
0.2%
0.124
0.2%
0.136
0.3%
0.144
0.2%
0.157
0.3%
0.162
 
0.1%
0.172
 
0.1%
0.182
 
0.1%
0.194
0.2%
0.23
0.1%
ValueCountFrequency (%)
6.381
< 0.1%
6.161
< 0.1%
6.111
< 0.1%
5.421
< 0.1%
4.961
< 0.1%
4.481
< 0.1%
4.071
< 0.1%
3.861
< 0.1%
3.791
< 0.1%
3.761
< 0.1%

clouds_all
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct99
Distinct (%)5.7%
Missing504
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean44.489726
Minimum0
Maximum100
Zeros504
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2025-11-02T21:14:16.840564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median35
Q396
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)96

Descriptive statistics

Standard deviation42.972438
Coefficient of variation (CV)0.96589576
Kurtosis-1.7393436
Mean44.489726
Median Absolute Deviation (MAD)35
Skewness0.20532117
Sum77946
Variance1846.6304
MonotonicityNot monotonic
2025-11-02T21:14:17.101430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0504
22.3%
100335
14.8%
9948
 
2.1%
145
 
2.0%
237
 
1.6%
536
 
1.6%
435
 
1.6%
333
 
1.5%
9830
 
1.3%
9623
 
1.0%
Other values (89)626
27.7%
(Missing)504
22.3%
ValueCountFrequency (%)
0504
22.3%
145
 
2.0%
237
 
1.6%
333
 
1.5%
435
 
1.6%
536
 
1.6%
617
 
0.8%
712
 
0.5%
812
 
0.5%
915
 
0.7%
ValueCountFrequency (%)
100335
14.8%
9948
 
2.1%
9830
 
1.3%
9722
 
1.0%
9623
 
1.0%
9510
 
0.4%
9413
 
0.6%
9313
 
0.6%
9213
 
0.6%
919
 
0.4%

weather_description
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.5%
Missing504
Missing (%)22.3%
Memory size133.0 KiB
sky is clear
739 
overcast clouds
305 
light rain
299 
broken clouds
149 
scattered clouds
118 
Other values (3)
142 

Length

Max length20
Median length16
Mean length12.524543
Min length10

Characters and Unicode

Total characters21943
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmoderate rain
2nd rowmoderate rain
3rd rowmoderate rain
4th rowmoderate rain
5th rowmoderate rain

Common Values

ValueCountFrequency (%)
sky is clear739
32.8%
overcast clouds305
13.5%
light rain299
13.3%
broken clouds149
 
6.6%
scattered clouds118
 
5.2%
few clouds70
 
3.1%
moderate rain65
 
2.9%
heavy intensity rain7
 
0.3%
(Missing)504
22.3%

Length

2025-11-02T21:14:17.358553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T21:14:17.556285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sky739
17.4%
is739
17.4%
clear739
17.4%
clouds642
15.1%
rain371
8.7%
overcast305
7.2%
light299
7.0%
broken149
 
3.5%
scattered118
 
2.8%
few70
 
1.6%
Other values (3)79
 
1.9%

Most occurring characters

ValueCountFrequency (%)
s2550
11.6%
2498
11.4%
c1804
 
8.2%
r1747
 
8.0%
l1680
 
7.7%
e1643
 
7.5%
a1605
 
7.3%
i1423
 
6.5%
o1161
 
5.3%
t919
 
4.2%
Other values (12)4913
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)21943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s2550
11.6%
2498
11.4%
c1804
 
8.2%
r1747
 
8.0%
l1680
 
7.7%
e1643
 
7.5%
a1605
 
7.3%
i1423
 
6.5%
o1161
 
5.3%
t919
 
4.2%
Other values (12)4913
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s2550
11.6%
2498
11.4%
c1804
 
8.2%
r1747
 
8.0%
l1680
 
7.7%
e1643
 
7.5%
a1605
 
7.3%
i1423
 
6.5%
o1161
 
5.3%
t919
 
4.2%
Other values (12)4913
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s2550
11.6%
2498
11.4%
c1804
 
8.2%
r1747
 
8.0%
l1680
 
7.7%
e1643
 
7.5%
a1605
 
7.3%
i1423
 
6.5%
o1161
 
5.3%
t919
 
4.2%
Other values (12)4913
22.4%

Interactions

2025-11-02T21:14:05.890167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:32.708694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:36.354932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:39.952648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:43.479601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:47.078357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:49.355692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:51.700220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:54.094148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:55.695609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:58.487994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:00.695228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:02.404427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:04.634933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:06.009927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:32.920558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:36.617988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:40.173691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:43.705957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:47.238338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:49.489927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:51.798048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:54.177978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:55.885870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:13:58.661874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:00.821216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:02.491104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:04.720733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T21:14:06.116847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-11-02T21:14:17.791428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Autoconsumo (kWh)HoraHorário Económico (kWh)Normal (kWh)clouds_alldtfeels_likehumiditypressurerain_1htemptemp_maxtemp_minweather_descriptionwind_speed
Autoconsumo (kWh)1.0000.095-0.6210.198-0.1080.0830.302-0.3640.054-0.0920.3440.3360.3100.0850.065
Hora0.0951.000-0.4140.518-0.0470.0140.246-0.196-0.013-0.1430.2620.2830.2750.000-0.043
Horário Económico (kWh)-0.621-0.4141.000-0.6960.0080.009-0.3520.259-0.003-0.020-0.384-0.382-0.3820.000-0.051
Normal (kWh)0.1980.518-0.6961.0000.072-0.0160.237-0.092-0.079-0.0680.2500.2580.2760.0340.040
clouds_all-0.108-0.0470.0080.0721.000-0.0150.3430.611-0.4250.4490.2850.2530.3360.6700.223
dt0.0830.0140.009-0.016-0.0151.0000.181-0.232-0.433-0.3190.1790.1720.1900.309-0.020
feels_like0.3020.246-0.3520.2370.3430.1811.000-0.020-0.1550.3160.9890.9540.9620.1620.168
humidity-0.364-0.1960.259-0.0920.611-0.232-0.0201.000-0.2550.291-0.103-0.114-0.0220.2700.209
pressure0.054-0.013-0.003-0.079-0.425-0.433-0.155-0.2551.000-0.443-0.131-0.123-0.1950.247-0.271
rain_1h-0.092-0.143-0.020-0.0680.449-0.3190.3160.291-0.4431.0000.3200.2970.3580.9190.436
temp0.3440.262-0.3840.2500.2850.1790.989-0.103-0.1310.3201.0000.9700.9670.1500.201
temp_max0.3360.283-0.3820.2580.2530.1720.954-0.114-0.1230.2970.9701.0000.9470.1610.218
temp_min0.3100.275-0.3820.2760.3360.1900.962-0.022-0.1950.3580.9670.9471.0000.1640.223
weather_description0.0850.0000.0000.0340.6700.3090.1620.2700.2470.9190.1500.1610.1641.0000.269
wind_speed0.065-0.043-0.0510.0400.223-0.0200.1680.209-0.2710.4360.2010.2180.2230.2691.000

Missing values

2025-11-02T21:14:07.523868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-02T21:14:07.680414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-02T21:14:07.920964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DataHoraDataHoraNormal (kWh)Horário Económico (kWh)Autoconsumo (kWh)dtdt_isocity_nametempfeels_liketemp_mintemp_maxpressuresea_levelgrnd_levelhumiditywind_speedrain_1hclouds_allweather_description
02023-01-01 00:00:002023-01-0100.0000.4670.0001.672531e+092023-01-01 00:00:00 +0000 UTClocal12.9312.7612.7213.431019.0NaNNaN95.02.023.72100.0moderate rain
12023-01-01 01:00:002023-01-0110.0000.5770.0001.672535e+092023-01-01 01:00:00 +0000 UTClocal13.4913.3813.4313.901018.0NaNNaN95.02.183.26100.0moderate rain
22023-01-01 02:00:002023-01-0120.0000.3460.0001.672538e+092023-01-01 02:00:00 +0000 UTClocal13.5513.4413.4814.821017.0NaNNaN95.02.882.44100.0moderate rain
32023-01-01 03:00:002023-01-0130.0000.2700.0001.672542e+092023-01-01 03:00:00 +0000 UTClocal13.6113.5112.0114.821016.0NaNNaN95.03.631.74100.0moderate rain
42023-01-01 04:00:002023-01-0140.0000.2520.0001.672546e+092023-01-01 04:00:00 +0000 UTClocal13.5913.4912.0114.821015.0NaNNaN95.04.581.13100.0moderate rain
52023-01-01 05:00:002023-01-0150.0000.2550.0001.672549e+092023-01-01 05:00:00 +0000 UTClocal13.7013.6113.1215.821014.0NaNNaN95.06.361.2399.0moderate rain
62023-01-01 06:00:002023-01-0160.0000.2560.0001.672553e+092023-01-01 06:00:00 +0000 UTClocal14.2214.1814.0416.121014.0NaNNaN95.08.551.20100.0moderate rain
72023-01-01 07:00:002023-01-0170.0000.2630.0001.672556e+092023-01-01 07:00:00 +0000 UTClocal15.3015.3114.7916.821015.0NaNNaN93.08.372.20100.0moderate rain
82023-01-01 08:00:002023-01-0180.2750.0000.0021.672560e+092023-01-01 08:00:00 +0000 UTClocal16.8016.5515.6616.821015.0NaNNaN77.08.292.20100.0moderate rain
92023-01-01 09:00:002023-01-0190.3310.0000.0001.672564e+092023-01-01 09:00:00 +0000 UTClocal16.6116.3114.7216.821014.0NaNNaN76.08.382.13100.0moderate rain
DataHoraDataHoraNormal (kWh)Horário Económico (kWh)Autoconsumo (kWh)dtdt_isocity_nametempfeels_liketemp_mintemp_maxpressuresea_levelgrnd_levelhumiditywind_speedrain_1hclouds_allweather_description
22462023-04-04 14:00:002023-04-04140.0000.0000.321NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22472023-04-04 15:00:002023-04-04150.0000.0000.319NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22482023-04-04 16:00:002023-04-04160.0000.0000.307NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22492023-04-04 17:00:002023-04-04170.0000.0000.330NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22502023-04-04 18:00:002023-04-04180.2070.0000.137NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22512023-04-04 19:00:002023-04-04190.9530.0000.012NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22522023-04-04 20:00:002023-04-04200.9150.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22532023-04-04 21:00:002023-04-04210.4790.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22542023-04-04 22:00:002023-04-04220.0000.4970.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22552023-04-04 23:00:002023-04-04230.0000.4870.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN